Systems and methods for steam generator tube analysis for detection of tube degradation

ABSTRACT

The systems and methods of the invention pertain to analyzing steam generator tube data for the detection of wear. Further, the invention is capable of performing a comparison of current tube signal data to baseline or historic tube signal data, e.g., from previous and/or the first, in-service inspection of the steam generator. The systems and methods are automated and can generate results to show potential tube-to-tube contact wear areas as well as the progression of tube-to-tube gap reduction within a steam generator tube bundle. In certain embodiments, the invention is capable of comparing current and historical eddy current data to determine the difference that may be related to degradation or other interested phenomena, and of processing and trending historical comparison results to establish normal variance and detect abnormal variances.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. Ser. No.13/951,984, filed on Jul. 26, 2013, entitled SYSTEMS AND METHODS FORSTEAM GENERATOR TUBE ANALYSIS FOR DETECTION OF TUBE DEGRADATION, whichapplication claims priority under 35 U.S.C. §119(e) to U.S. ProvisionalPatent Application Ser. Nos. 61/711,875, filed Oct. 10, 2012, entitledU-BEND ANALYSIS FOR DETECTION OF TUBE-TO-TUBE CONTACT WEAR ANDTUBE-TO-TUBE PROXIMITY; 61/755,610, filed Jan. 23, 2013, entitledAUTOMATED HISTORY COMPARISON; and 61/755,601, filed Jan. 23, 2013,entitled AUTOMATED HISTORY TRENDING.

BACKGROUND

1. Field

This invention pertains in general to nuclear power plants and, moreparticularly, to systems and methods for evaluating the tubes of a steamgenerator of a nuclear power plant.

2. Description of Related Art

Nuclear power plants can generally be stated as comprising a reactorthat includes one or more fuel cells, a primary loop that cools thereactor, and a secondary loop that drives a steam turbine which operatesan electrical generator. Such nuclear power plants typicallyadditionally include a heat exchanger between the primary and secondaryloops. The heat exchanger typically is in the form of a steam generatorwhich comprises tubes that carry the primary coolant and a plenum thatcarries the secondary coolant in heat-exchange relationship with thetubes and thus with the primary coolant.

As is also generally known, the tubes of a steam generator are subjectto wear from mechanical vibration, corrosion, and other mechanisms. Itthus is necessary to periodically inspect the tubes of a steam generatorfor wear in order to avoid failure of a tube which might result innuclear contamination of the secondary loop, by way of example. Steamgenerator tube-to-tube contact wear generally is a concern in thenuclear industry. Both manual and automated processes are known todetect and address this concern. However, these known manual andautomated processes have been shown to not be reliable. Methods ofmeasuring tube-to-tube proximity (i.e., the spatial relationship betweentwo adjacent SG tubes), a potential precursor for tube-to-tube contactwear, is cumbersome and also has been shown to not be reliable.Guidelines, analysis training and process changes have been implementedin the art with varying levels of success.

One method of inspecting the tubes of a steam generator involves theinsertion of an eddy current sensor into one or more of the tubes and toreceive from the eddy current sensor a signal which typically is in theform of a voltage and a phase angle. An analyst reviewing the signaldata typically must possess a high degree of expertise in order toaccurately ascertain from the signal data the current condition of thetubes of the steam generator. A typical steam generator might possessbetween three thousand and twelve thousand tubes, by way of example,with each tube being several hundred inches in length. Thus, the reviewof eddy current data can require the expenditure of large amounts oftime by an analyst. While certain testing protocols may require thetesting of fewer than all of the tubes of a steam generator, dependingupon the particular protocol, the time in service, and other factors,the analysis of such data still requires significant time and expense.

Among the difficulties involved in the analysis of eddy current data isthe determination of whether a signal is indicative of a possiblefailure of a portion of a tube or whether the signal is not indicativeof such a failure. Each tube of a steam generator typically has a numberof bends and a number of mechanical supports. In passing an eddy currentsensor through such a tube, the signal from the eddy current sensor willvary with each mechanical support and with each bend, and the signalalso will vary in the presence of a flaw such as a crack or a dent inthe tube. As such, the difficulty in analysis involves the ability todetermine whether a change in a signal from an eddy current isindicative of a known geometric aspect of a tube such as a bend orsupport, in which case further analysis of the signal typically isunnecessary, or whether the change in signal from the eddy currentsensor is indicative of a crack or a dent, in which case furtheranalysis of the signal typically is necessary.

To reduce the impact of the unwanted signals, the concept of combiningdata at different inspection frequencies, i.e., mixing, was implemented.By mixing data from different frequencies, an unwanted response can beminimized and a degradation response enhanced. The additional dataprovided by multi-frequency data acquisition coupled with the capabilityto eliminate unwanted signals places more information in the inspectionresults. This information is useful for assessing the reliability of thesteam generators to operate during a fuel cycle and, for determiningwhether repairs should be performed in order to avoid costly and timeconsuming failures.

Outside Diameter Stress Corrosion Cracking (ODSCC) events that haveoccurred in operating nuclear power plants has increased the detectionrequirements of small and shallow signals. Such requirements posesignificant challenges to the eddy current bobbin coil inspectiontechnique which is commonly used for full length inspection of steamgenerator tubing. The traditional manual method of evaluating andcomparing bobbin coil signals for change between inspections is timeconsuming and subjective. Alternate inspection techniques, such asrotating pancake coil probe and array coil probe, have shown to becostly and time consuming. Procedural control (analysis guidelines),analyst training and process changes have been introduced to addressthis issue but generally have not been proven to be entirely successful.

It is, therefore, an object of this invention to provide reliablesystems and methods to analyze steam generator U-bend region bobbin coildata for accurate detection of tube-to-tube contact wear andtube-to-tube proximity. It is desired that these systems and methods areautomated and the results can generate an illustration, e.g., map, toshow the potential tube-to-tube contact wear areas as well as theprogression of tube-to-tube gap reduction within a steam generator tubebundle. It is anticipated that the results obtained from these systemsand methods and the illustration of the results will providecomprehensive steam generator repair solutions that will precludefailures and unplanned shutdowns that are time-consuming and costly.

It is another object of the invention to develop an automated processincluding signal processing techniques to identify and evaluate signalsfor change over a period of time to provide a consistent and reliableanalysis of steam generator bobbin coil data from one in-serviceinspection to subsequent inspections.

It is another object of the invention to develop an automated processincluding trending of historical comparison results to establish normalvariance and to detect abnormal variances.

SUMMARY

In one aspect, the invention provides a method of employing at least oneeddy current sensor and at least one digital computing device tonon-destructively assess a current condition of a number of tubes of asteam generator of a nuclear power plant. The method includes collectingat a first time with a digital computing device and using an eddycurrent sensor received in and advanced through each of at least some ofthe number of tubes a historic data set for each of at least some of thenumber of tubes; collecting at a second time with a digital computingdevice and using an eddy current sensor received in each of at leastsome of the number of tubes and advanced there through a current dataset for each of at least some of the number of tubes; injecting at leasta portion of the historic data set into a corresponding at least portionof the current data set with a digital computing device to form a mergeddata set; suppressing from the merged data set aspects that were presentin the historic data set; and generating another data set representativeof a change in condition of a tube of the number of tubes between thefirst time and second time.

In another aspect, the invention provides a method of employing at leastone eddy current sensor and at least one digital computing device tonon-destructively assess a current condition of a number of tubes of asteam generator of a nuclear power plant. The method comprisescollecting at a first time with a digital computing device and using aneddy current sensor received in and advanced through each of at leastsome of the number of tubes a historic data set for each of at leastsome of the number of tubes; collecting at a second time with a digitalcomputing device and using an eddy current sensor received in each of atleast some of the number of tubes and advanced there through a currentdata set for each of at least some of the number of tubes; measuringnoise window of the historical data set to determine a historical noisebaseline; storing the historical noise baseline in the digital computingdevice; measuring noise window of the current data set to determine acurrent noise baseline; storing the current noise baseline in thedigital computing device; comparing the historical noise baseline andthe current noise baseline to determine a difference; and identifying aregion of a baseline shift based on the difference to show potentialtube to tube contact or long taper wear.

In another aspect, the invention provides a method of employing at leastone eddy current sensor and at least one digital computing device tonon-destructively assess a current condition of a number of tubes of asteam generator of a nuclear power plant. The method comprisescollecting at a first time with a digital computing device and using aneddy current sensor received in and advanced through each of at leastsome of the number of tubes a historic data set for each of at leastsome of the number of tubes; collecting at a second time with a digitalcomputing device and using an eddy current sensor received in each of atleast some of the number of tubes and advanced there through a currentdata set for each of at least some of the number of tubes; measuring asignal of interest using the historical data set and recording at leastone signal characteristic selected from the group consisting of signalamplitude, phase, pattern, signal width, and signal area; storing the atleast one signal characteristic in database; measuring a signal ofinterest using the current data set and recording at least one signalcharacteristic selected from the group consisting of signal amplitude,phase, pattern, signal width, and signal area; storing the at least onesignal characteristic in database; generating a trending plot comparisonbetween the at least one signal characteristic for the historical andcurrent data sets to determine variances there between; determining anormal variances based on the historical data set to determine a normalvariance zone; and determining an abnormal variance based on the currentdata set if the comparison is different than the normal variance.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the invention can be gained from thefollowing description of the preferred embodiments when read inconjunction with the accompanying drawings in which:

FIG. 1 is a flowchart depicting certain aspects of the invention;

FIG. 2 is a flowchart of steps included in the method of this invention;

FIG. 3 is a flowchart depicting certain other aspects of the invention;and

FIG. 4 is a flowchart depicting certain other aspects of the invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT

The systems and methods of the invention pertain to analyzing steamgenerator U-bend region bobbin coil data for the detection oftube-to-tube contact wear. Also, the systems and methods can detect andmeasure undesirable tube-to-tube proximity conditions that may causefuture tube-to-tube contact wear issues. The invention includes severaladvanced signal processing techniques that remove the U-bend regionanti-vibration bar structure interference (upon the bobbin coil signal).Further, the invention is capable of performing a comparison to signalsfrom baseline data obtained from previous, e.g., the first, in-serviceinspection of the steam generator. Any change in data, e.g., bobbin coilsignals, that exhibit flaw-like characteristics are subjected toadditional evaluation or testing with an alternate inspection technique.The systems and methods are automated and can generate the results insuch a form, e.g., a map, to show potential tube-to-tube contact wearareas as well as the progression of tube-to-tube gap reduction within asteam generator tube bundle. This complete steam generator tube-to-tubecontact wear and tube bundle proximity analysis provides informationnecessary to devise and develop comprehensive repair solutions. Thesystems and methods of the invention are capable of processing andtrending historical comparison results to establish normal variance andto detect abnormal variances.

In general, the invention includes performing a total signal and noiseanalysis from baseline or first in-service inspection, determiningstraight section, support, and bend tube information, storing this tubeinformation, creating processed channels with the structure interferingsignals removed to allow a true measurement of the baseline signal, andstoring the baseline true measurement in a database. The total signaland noise analysis can be performed using suitable software, such as butnot limited to Real Time Automated Analysis (RTAA). RTAA is an automatedanalysis process which is generally known in the art for use ininspecting steam generator tubes for degradation. A true measurement ofthe baseline signal can be accomplished using rolling noise windowmeasurement. In certain embodiments, this concept and method are used togenerate a baseline inspection of tube U-bends and tube supports andtransitions to determine wear. In other embodiments, this concept andmethod is used to generate a baseline inspection of straight length tubesections to determine whether the tube is bent such that it may contactadjacent tubes.

During subsequent steam generator tube inspections, a total signal andnoise analysis, e.g., current, is performed using RTAA and a truemeasurement of the U-bend signal, e.g., current, is obtained usingrolling noise window measurement. This current data is then merged andcompared with previous or historical data stored in the database, e.g.,the baseline or first in-service inspection data, to identify anychanges. For each current tube signal, the same tube signal informationfrom the baseline database is examined for comparison.

In certain embodiments, the baseline and current tube inspection dataeach can be obtained in accordance with the following process. An eddycurrent sensor is received within the interior of an elongated tube of asteam generator and is passed through the interior of the tube along thelongitudinal extent thereof. Longitudinal movement of the sensor can beperformed manually, although it can also advantageously be performed bya robotically-controlled advancement mechanism that advances the eddycurrent sensor at a controlled rate and that is capable of providing adata stream component representative of the longitudinal distance of theeddy current sensor along the tube at any given time. Other data streamsfrom the eddy current sensor typically comprise a voltage component thatcharacterizes amplitude and another component that characterizes a phaseangle. Although many methodologies can be employed for the storage andanalysis of such data streams, one methodology involves the storage ofvoltage and phase data at given points along the longitudinal length ofa tube. Typically, thirty data points per inch are collected and stored,but other data distributions and densities can be employed withoutdeparting from the present concept.

As is generally understood, a typical steam generator includes a plenumthat encloses perhaps four thousand to twelve thousand individual tubesthat each comprise a hot leg and a cold leg that pass through a tubesheet, which is itself a slab of metal that is typically twenty or moreinches thick. Each tube may be several hundred inches long and haveeither a single U-bend or a pair of elbow bends, although othergeometries can be employed without departing from the present concept.Each such tube typically additionally includes twenty to thirty physicalsupports of differing geometries. During initial manufacture, the hotand cold legs of each tube are assembled to the tube sheet by receivingthe two ends of the tube in a pair of holes drilled through the tubesheet and by hydraulically bulging the ends of the tube into engagementwith the cylindrical walls of the drilled holes.

While the geometry of each tube of a steam generator typically isdifferent from nearly every other tube of the steam generator, theoverall construction of the steam generator enables generalizations tobe made with regard to the geometry of the tubes as a whole. That is,each tube can be said to include a pair of tube sheet transitions at theends thereof which typically are characterized by an eddy current sensorvoltage on the order of thirty (30.0) volts. Between the two tube sheettransitions are various straight runs, supports, and bends. The typicaleddy current voltage for a straight section of tube is 0.05 volts, andthe typical voltage for a bend of a tube is 0.1 volts. A typical voltagefor a support may be 0.2 volts, but various types of supports can existwithin a given steam generator, all of which may produce differentcharacteristic voltages.

Advantageously, however, the various arrangements of straight sections,supports, and bends as a function of distance along a tube are of alimited number of permutations within any given steam generator. Assuch, a location algorithm is advantageously developed from the knowngeometry of the steam generator and the historic data that can becollected from the steam generator, wherein an input to the algorithm ofa series of voltage and distance values can identify a particular regionof interest (ROI) of a tube that is under analysis. That is, the wearthat is experienced by a tube often can occur at a tube sheettransition, at a location of attachment of a tube to a mechanicalsupport, at a transition between a straight section and a bend in atube, or at other well understood locations. The various segments of agiven tube can be divided into various regions of interest (ROIs) whichcan be identified during data collection with a high degree of accuracybased upon the details of the steam generator geometry that areincorporated into the location algorithm. As such, by inputting voltage,phase, and distance data into the location algorithm, the locationalgorithm can identify a specific segment and thus, physical ROI of thetube being analyzed.

The invention can also be said to include the development of a model forthe steam generator that includes baseline parameters such as voltageand phase for each of a plurality of exemplary ROIs that exist in theparticular steam generator. Advantageously, and as will be set forth ingreater detail below, the model additionally includes exception data forparticular ROIs of particular tubes that have voltage and/or phase angleparameters that would exceed the baseline parameters of thecorresponding ROI of the model but that are nevertheless acceptable,i.e., the signals from such ROIs are not themselves indicative of flawsthat require further evaluation by an analyst.

The baseline parameters for the various exemplary ROIs of the model canbe established in any of a variety of ways. In the exemplary embodimentdescribed herein, the various baseline parameters for the variousexemplary ROIs of the model are established based upon theoreticalevaluation of tubes and their ROIs, as well as experimental data basedupon eddy current analysis of actual tubes and their physical ROIs. Thedirect physical analysis of tubes such as through the collection of eddycurrent data of individual tubes of a steam generator advantageouslyenables the collection of data with respect to typical ROIs that can beemployed in establishing baseline parameters for exemplary ROIs of themodel. Such direct physical analysis of tubes can additionally beemployed to collect data that is later stored as exception data forparticular ROIs of particular tubes.

Additionally and advantageously, such direct collection of eddy currentdata during the initial manufacture of a steam generator can enable aninitial evaluation of each tube to assess whether the tube should berejected or whether the data appears to be unreliable and should berecollected. A tube may be rejected if the data suggests that it isdefective in manufacture. On the other hand, the data may need to berecollected if it appears that the eddy current sensor was functioningimproperly or if other data collection aspects appear to be erroneous orunreliable.

FIG. 1 generally depicts an exemplary methodology for the collection oftube data which enables the development of a model of a steam generatorand the development of a location algorithm that is based upon thegeometry of the steam generator. Processing begins, as at 104, whereeddy current data is collected for a given tube of the steam generator.As mentioned herein, the data stream typically will include componentsof voltage, phase, and distance, all of which can be detected as acontinuous signal or as a discrete set of data points along the lengthof the tube.

In FIG. 1, processing continues at 108, where it is determined whetherthe data derived from the eddy current sensor signal is potentiallyunreliable. For instance, if the data suggests a possible datacollection error, processing continues as at 112, where the tube data isrejected, and the tube is retested. Processing thereafter wouldcontinue, as at 104. However, if at 108 the data is not determined to beunreliable, processing continues, as at 116, where it is determinedwhether the tube data derived from the eddy current signal exceeds anacceptance threshold, such as would indicate that the tube itself ismechanically or otherwise defective. In the event that the data exceedsan acceptance threshold, the tube is rejected, as at 120.

If the tube data does not exceed the acceptance threshold at 116,processing continues, as at 124, where it is determined whether anyportions of the tube data exceed what should theoretically be thebaseline parameters of that portion of the tube, i.e., the baselineparameters for the corresponding exemplary ROI of the model of the steamgenerator. By way of example, it may be determined that the physical ROIof the tube that is under analysis includes a physical support and theeddy current sensor is indicating a voltage of 0.4 volts. While ananalyst may determine that the voltage that would typically be expectedfor such an ROI is 0.2 volts, the analyst may nevertheless determinethat the particular physical ROI is acceptable and that the voltage of0.4 volts is an acceptable anomaly. In such a circumstance, the data forthe particular ROI for this particular tube will be saved, as at 132, asa portion of an exception data set. In this regard, it is reiteratedthat the tube or its data would already have been rejected, as at 112 or120 respectively, if the data for the aforementioned ROI suggested thatthe ROI would be unacceptable.

Referring to FIG. 1, processing continues from both 124 and 132 onwardto 128 where the tube data is stored in a data set. It is thendetermined, as at 136, whether further tubes require eddy currentanalysis as set forth above. If further tubes await testing, processingcontinues, as at 104, with a new tube. Otherwise, processing continues,as at 140, where the model of the steam generator is developed with aset of baseline parameters for each of a plurality of exemplary ROIs.The model further includes the aforementioned exception data for one ormore particular ROIs of one or more particular tubes. It is understoodthat the inclusion as at 140 of the development of the steam generatormodel at this particular location within the exemplary methodology isintended to be merely an example of a point at which a model of thesteam generator can be developed. It is understood that with analyticalmethods, at least an initial model of the steam generator can bedeveloped, with the experimental collection of tube data from 104through 132 being supplied to the model to provide refinement of themodel and to provide exception data. It thus is understood that themodel of the steam generator can be developed in whole or in part at anytime depending upon the data and the analysis that are available.

Referring to FIG. 1, processing continues to 144 where the locationalgorithm which identifies various ROIs can be developed based upon thegeometry of the steam generator and other factors. As was mentionedelsewhere herein with respect to the development of the model of thesteam generator, the location algorithm can likewise be developed inwhole or in part at any time depending upon the analytical andexperimental data that is available in the development process depictedgenerally in FIG. 1. When completed, the location algorithmadvantageously can receive a data stream from an eddy current sensorwithin the tube of the steam generator and can employ the voltage,phase, and distance data components to identify any of a variety ofexemplary ROIs that are stored within the model of the steam generator.That is, the location algorithm can employ the eddy current signalwithin a tube of the steam generator to identify a particular segment ofthe tube and thus a physical ROI of the tube, and the location algorithmcan additionally identify from the model that was developed of the steamgenerator a corresponding exemplary ROI and its baseline parameters forcomparison with the eddy current signal that is being collected from thephysical ROI.

The testing of the tubes of a steam generator is depicted in anexemplary fashion in FIG. 2. It is understood that the operationsdepicted generally in FIG. 1 typically will occur at a first time andwill be in the nature of a historic data set. The operations occurringin FIG. 2 typically occur at a second, subsequent time and may morelikely be directed toward current or present testing of a steamgenerator. Processing begins, as at 204, where a signal is extractedfrom a tube of the steam generator. The signal from the eddy currentsensor is processed with the aforementioned location algorithm, as at208, to determine the physical ROI that is the source of the signal thatis being collected from the tube under analysis. The location algorithmthen employs, as at 212, the signal from the eddy current sensor toretrieve from the model an exemplary ROI that is determined tocorrespond with the physical ROI that has been located by the locationalgorithm. It is then determined, as at 216, whether the signal data forthe physical ROI exceeds the baseline parameters of the exemplary ROIfrom the model that was identified and retrieved at 212. If it isdetermined at 216 that the eddy current signal for the physical ROI doesnot exceed the baseline parameters of the exemplary ROI, processing willcontinue, as at 220, where no further action will be taken with respectto this particular physical ROI. That is, no additional analysis will betriggered for this particular physical ROI, thereby avoiding the needfor an analyst to perform any evaluation with respect to this physicalROI.

It is then determined, as at 224, whether the end of the tube underanalysis has been reached. If so, the analysis of the current tube ends,as at 228. Another tube can then be analyzed. However, if the end of thetube is determined at 224 to not be reached, processing continues, as at204, where the eddy current signal is continued to be extracted from thetube under analysis.

The aforementioned baseline parameters of the various exemplary ROIs ofthe model can be developed in any of a variety of fashions. Mosttypically, the baseline parameters will be developed with the use oftheoretical data and experimental data, as suggested above. Forinstance, the typical eddy current voltage that one might expect todetect from a straight section of a tube is 0.05 volts, and the datacollection effort depicted generally in FIG. 1 might demonstrate, by wayof example, that the tested voltage values for each straight segment ofeach tube is 0.08 volts or less. As such, the baseline voltage for anexemplary ROI that corresponds with a straight section of a tube mightbe established 0.1 volts. This would enable all physical ROIs that arestraight sections of tubes to, in their original condition, not exceedthe baseline parameter of 0.1 volts and thus not trigger the need forfurther analysis, as at 220.

Similarly, the typical eddy current sensor voltage that one might expectfrom a curved section of a tube is 0.1 volts, and the baseline parameterfor experimental ROIs of bend segments of each tube might be establishedat 0.2 volts. Physical supports typically generate an eddy currentvoltage of 0.2 volts, so the baseline parameter for a physical supportROI might be established at 0.3 volts. Such baseline parameterstypically will be based upon the various specifications of the steamgenerator and the nuclear power plant, along with theoretical andexperimental data regarding the steam generator. It is understood,however, that the baseline parameters typically will be selected suchthat an eddy current sensor signal that exceeds a baseline parameter isworthy of further evaluation by an analyst, assuming that applicableexception data for the particular physical ROI does not already exist inthe model. That is, the baseline parameters desirably will be selectedsuch that no further action is triggered when the eddy current sensorsignals are below that which should reasonably trigger further analysisof the particular physical ROI. It is understood, however, that variousmethodologies may be employed for establishing the baseline parametersof the exemplary ROIs without departing from the present concept.

It is also noted that the baseline parameters can include voltages,phase angles, pattern data, and any other type of characterization of anexemplary ROI that may be appropriate. The degree of sophistication ofthe baseline parameters is limited only by the ability to collect andanalyze data regarding the tubes. As such, the baseline parameters of anexemplary ROI can be determined to be exceeded if any one or more of thevarious parameters in any combination are exceeded by a signal withoutlimitation. Additionally or alternatively, the baseline parameters couldhave an even greater degree of sophistication wherein certaincombinations of parameters need to be exceeded in a certain fashion forthe system to trigger the need for further analysis, by way of example.

On the other hand, if it is determined, as at 216, that the signal forthe physical ROI exceeds in some fashion the baseline parameters of theidentified corresponding exemplary ROI, processing continues, as at 230,where it is determined whether exception data exists for the physicalROI that is under analysis. As mentioned elsewhere herein, the exceptiondata advantageously will be a part of the model of the steam generator.If such exception data is determined at 230 to exist, processingcontinues, as at 234, where it is determined whether the signal from thephysical ROI exceeds the exception data by a predetermined threshold.That is, it is not expected that the physical ROI that is the subject ofthe exception data will remain unchanged during the life of the steamgenerator, and rather it is expected that the physical ROI might degradeover time due to wear, corrosion, etc. Since the physical ROI hasalready been determined at the time of taking the historic data set tohave a signal which exceeds the baseline parameters that would otherwisebe expected from a similar ROI, the threshold that is already built intothe baseline parameters is unlikely to be useful in evaluating theparticular physical ROI that is the subject of the retrieved exceptiondata. As such, a separate threshold is established based upon variousfactors which, if exceeded by the present signal from the physical ROI,will trigger further analysis as at 238, of this particular physicalROI. Such further analysis likely will be manual evaluation by ananalyst. On the other hand, if it is determined at 234 that the signalfrom the physical ROI fails to exceed the retrieved exception data bythe predetermined threshold, processing continues, as at 220, where nofurther action is taken for this particular physical ROI. Furtherevaluation by an analyst is also triggered, as at 238, if it isdetermined, as at 230, no exception data exists for this particularphysical ROI.

It is noted that an additional notification can be triggered if thebaseline parameters of the exemplary ROI are exceeded by a significantamount, or if the predetermined threshold for the exception data isexceeded by a significant amount, in order to alert an analyst that anincreased level of attention should be directed to a particular physicalROI, for example. In the exemplary embodiment depicted herein, forinstance, further analysis is triggered if either the baselineparameters of the exemplary ROI or the predetermined threshold of theexception data is exceeded in any fashion. However, an additionalnotification can be generated if the signal exceeds the baselineparameters or the predetermined threshold of the exception data by 25%,by way of example. It is understood that any type of criteria can beemployed to trigger such heightened further analysis.

It therefore can be seen that the eddy current data that is collectedfrom a tube under analysis is evaluated using the model that includesexemplary ROIs with baseline performance parameters and further includesexception data for ROIs of particular tubes, with the result being thetriggering of further analysis such as evaluation by an analyst only inspecific predefined circumstances such as would occur at 238. As such,the manual evaluation effort that is required of an analyst using theexemplary methods set forth herein is greatly reduced compared withknown methodologies.

It is noted that the exemplary method depicted generally in FIG. 2envisions a real-time automated analysis system wherein a signal that iscollected from a tube is input directly into the location algorithm andis evaluated as it is collected. It is understood, however, thatdifferent methodologies may be employed. For instance, the data from oneor more tubes can be collected and stored and then evaluated as a wholerather than being analyzed on a real-time basis. Other variations can beenvisioned that are within the scope of the present concept.

FIG. 3 generally depicts an exemplary methodology for analyzing signalsof interest collected from regions or areas of interest of tubes in asteam generator that is undergoing analysis. As such, another aspect ofthe invention is to collect historic tube signal data for each tube,e.g., region or area of interest of a tube of a steam generator, as at304, and employ the historic tube data for use at a later time incomparison with tubes, e.g., region or area of interest of a tube of asteam generator that is under analysis after a period of use.Advantageously, the historic data shares certain aspects with currentcollected data. The method advantageously merges the historical andcurrent signal data, and suppresses from the current signal data anyaspects that were also present in the historic tube data in order togenerate an improved simpler signal that is indicative of a change incondition of the area or region of a tube under analysis. The historictube signal data can be taken at the time of manufacture of the steamgenerator or can be taken at a later time, such as during an in-serviceinspection of a steam generator.

The historic tube signal data that is collected at 304 duringmanufacture or in-service inspection of a steam generator is then storedfor future retrieval and comparison with subsequently collected dataduring a current testing operation. That is, current tube signal data iscollected, as at 308, for a given tube of a steam generator. Thehistoric tube data for the same tube is retrieved. It is typically thecase that some type of scaling with respect to either the current dataor the historic data will occur, as at 312, to permit comparison. By wayof example, it may be necessary to reduce or increase or otherwisemanipulate all of the values of either the current or historic data setssince different eddy current sensors or other instrumentation wereemployed to take both sets of data or because of other differingoperating parameters between the eddy current sensors employed to takethe historic and the current tube data. Other types of scaling may benecessary if the data points of the historic tube data do not matchsufficiently with the data points of the current tube data. As mentionedelsewhere herein, data may be taken at thirty locations per inch,although forty-five locations per inch may likewise be employed, as canother data signal densities. Still other scaling may be required if thedirection of movement of the eddy current sensor is different betweenthe historic data and the current data. For example, the historic datamay have been based upon longitudinal movement of an eddy current sensorin a direction from the tube sheet toward the tube sheet transition,whereas the current data may involve an eddy current sensor that ismoving in a direction from the tube sheet transition toward the tubesheet. Regardless of the nature of the historic and current tube data,scaling or other mathematical manipulations may be performed at 312 topermit comparison between the two.

The historic tube signal data is then injected into the current tubesignal data, as at 316. That is, these two data sets, i.e., the historicand the current tube data, are combined to form a merged tube signaldata set. The merged data set is subjected to a suppression step. Theinjected historic tube data of the merged data set is suppressed, as at318. The suppression process employs the ANSER ALFS (Axial Look ForwardSuppression) software that is licensable from Westinghouse ElectricCompany, LLC, Cranberry Township, PA. The ANSER software suppressesidentified signals (e.g., tube sheet transition) and enhancesdegradation signal (e.g., ASME 20% flaw). Other suppression techniquesand software may be used such as, but not limited to, simple mix.However, in certain embodiments wherein multiple year comparisons are tobe performed, the ALFS is preferred because it has this capability. Thesuppression output is validated, as at 320. Validation providesverification of suppression of common mode signal not to exceed apre-determined voltage (such as 0.5 V) and enhancement does not distortthe sample defect as well as preserves its phase and voltage (e.g., 20%hole signal at greater than 4V and more than 140 degrees) and thus,increases confidence in the ability of the process to accurately detectdegradation. As a result, a new signal is generated which isrepresentative of the change in condition of the tube, e.g., area/signalof interest, that is under analysis between the time at which thehistoric tube data was collected, such as at the time of manufacture orduring an in-service inspection, and the time at which the current tubedata is collected.

The injection and suppression of the tube data, as at 316 and 318, canbe performed by employing suitable software, such as but not limited toData Union Software (DUS) which is licensable from Westinghouse ElectricCorporation, LLC, Cranberry Township, PA. The DUS software generallyprovides for combining, e.g., merging, mixing or injecting, two datasets, e.g., historic and current tube signal data, to produce a data setthat is a combination of the two data sets. Employing DUS providesadvantages over prior art software, such as but not limited to, theability to: (i) process historical and current data sets that may becollected using different instruments and operating parameters; (ii)subject both the historic and current data sets to a common mode datanoise environment in order to suppress any common mode signal; (iii)perform suppression on merged data to increase the speed and efficiencyof the process, and (iv) apply in a cumulative manner to permit multiplesets of historic data to be compared with current data with efficiencyand high accuracy.

A flow chart of the overall data combination process of this inventionis shown in FIG. 4. Referring to FIG. 4, the first steps 12 and 14 inthe data injection process are to define the calibration ornormalization parameters for each Data Set A and B, wherein A representshistoric tube sheet transition data and B represents current tube sheettransition data. Preferably, at least one of the standard holes ornotches in a calibration standard used for this purpose should beidentical for both data sets. If they are not identical, mathematicalmodels can be used as a basis for interpolation of one or the other ofthe data sets. The calibration standard is a specimen created inaccordance with the ASME code. Each of the data sets is then calibratedso that the reference discontinuity response for the two data sets, Aand B, is identical. For bobbin coil eddy current data, this can beaccomplished by setting the voltage of the 20% holes, i.e., 20%through-wall flaw, to 4 volts and the phase of the through-wall hole toa 40° rotation. For rotating probe data, the through-notch in thecalibration standard can be used to set the phase and amplitude. Formost applications, it can be assumed that the accuracy of the standardis sufficient so that cross calibration of the standards is notrequired.

After the calibration parameters are established, the segment of datafrom data set A to be inserted into data set B is selected and stored instep 16, shown in FIG. 4. In the DUS system, this is accomplished bysuperposition of the calibration signal into a duplicate segment ofcurrent data. This data then can be used for enhancement of historiccomparison, as well as validation to ensure the result of comparison hasnot distorted the injected calibration signal. The data segment A alongwith the calibration parameters for that segment determined in step 12is then stored in a file. Multiple segments from the same tube orspecimen can be stored and each identified with a ROW/Col. and sequencenumber as represented by step 18 in FIG. 4.

Referring to FIG. 4, after the segments of interest have beenidentified, the data set B in which the segment is to be inserted isread into the machine. The location where the segment is to be insertedis chosen in step 20. In the DUS system, this is accomplished byduplicating a segment of current data in the free span that is free ofstructure signal or other anomalies. Once the segment A data isselected, the appropriate calculations are made in step 22, based uponthe calibration parameters, to rotate and scale the segment A data sothat it has the same calibration factors as data set B. The thusnormalized segment A data is then added to the displayed data set instep 24 shown in FIG. 4. To display the results with the DUS system,data set B must be reread into the machine. This process can be repeatedwith different sets of data from different years thus, permittinghistorical comparison of multiple years.

In the foregoing embodiment, the segment A data is added into thedisplayed data. If desired, the segment A data could equally wellreplace some of the displayed data in set B. Furthermore, since the dataset that is being displayed is the file that is modified, it isimportant that the combination process take place on a copy of the dataand not the original file. Once the combination process is complete, thenew data set can be manipulated in the same way as any other data set.No knowledge of the data combination process is retained in the combinedfile.

In certain embodiments, it may be desirable to amplify one or moreportions of the new signal that is generated. Such an amplified signalwould emphasize those aspects of the new signal that would be even moreindicative of a change in the condition of the tube sheet transitionbetween the time the historic data was collected and the time that thecurrent data is collected.

The signal is then submitted, as at 324, for evaluation. Such evaluationmay be performed automatically or may be performed manually by ananalyst. It is then determined whether any additional tubes of the steamgenerator require analysis with respect to their tube region. If furthertubes require analysis, processing continues. Otherwise, processingends.

In this regard, it is understood that the aforementioned tube analysiscan be performed as a part of the analysis depicted generally in FIG. 2or can be performed separately. In this regard, the historic tube datathat was collected at 304 potentially can be saved as part of the modelof the steam generator, particularly as a special part of the exceptiondata set. As such, it may be possible to completely analyze a tube fromone tube sheet transition through its longitudinal extent and to itsopposite tube sheet transition using the teachings herein. As mentionedelsewhere herein, however, it is possible to analyze the tube sheettransitions separately from the other portions of the tubes, as may bedesired.

As previously mentioned, the analysis methodology depicted in FIG. 3 isapplicable to signals of interest collected from regions or areas ofinterest of the tubes of a steam generator. Thus, the methodology can beused throughout the steam generator tube to analyze dents, supports,straight length segments, tube sheets, transitions, and the like. Incertain embodiments, the methodology is useful in analyzing tube sheettransition regions. Due to the thickness of the tube sheet, the eddycurrent data that is collected from a tube in the tube sheet transitionregion typically is of a voltage far in excess of any of the baselineparameters of any of the exemplary ROIs. Moreover, the variation in eddycurrent voltage from one tube sheet transition to another is also far inexcess of any baseline parameter of an exemplary ROI. For instance, andhas been mentioned elsewhere herein, the eddy current voltage for a tubesheet transition might be on the order of thirty (30.0) volts. The eddycurrent voltage of another tube sheet transition might be 25.0 volts,and that of another tube might be 35.0 volts. Since the eddy currentvoltages at tube sheet transitions are one or more orders of magnitudegreater than any voltage that would be generated in other portions ofthe tube, i.e., portions other than the tube sheet transition, themethod depicted in FIG. 3 is useful to facilitate the analysis ofsignals collected from tube sheet transitions of a steam generator thatis undergoing analysis. In general terms, it is understood that the eddycurrent signals from tubes in the tube sheet transition area of a steamgenerator are of a voltage that is sufficiently high that the portion ofthe eddy current signal which might indicate a possible flaw, i.e., thesignal of interest, which might be on the order of 0.1 volts, is far toosmall in comparison with the overall eddy current signal to be easilydetected or evaluated.

It is also noted that the teachings employed herein can be applied in acumulative fashion to permit multiple sets of historic data to becompared with current data. That is, historic data can be taken at afirst time, such as at the time of manufacture of a steam generator orat an in-service inspection, and such historic data can be employedduring a subsequent evaluation of the steam generator tubes. The datathat is developed during such a subsequent evaluation may then be storedas a second historic data set. Both historic data sets can then becompared with data that is collected during a further inspection of thesteam generator to enable the change in the condition of various tubesto be charted as a function of time over the course of severalinspections that occur at several different times. Other uses of thedata can be envisioned.

It is understood that the analysis described herein can be performed ona digital computer or other processor of a type that is generally known.For instance, such a computer might include a processor and a memory,with the memory having stored therein one or more routines which can beexecuted on the processor. The memory can be any of a wide variety ofmachine readable storage media such as RAM, ROM, EPROM, EEPROM, FLASH,and the like without limitation. The signal from the eddy current sensormight be received by an analog-to-digital converter which provides adigital input to the computer for processing and storage of the signals.The historic and current data can be stored on any such storage mediaand can potentially be transported or transmitted for use on othercomputers or processors as needed.

In general, the invention includes, but is not limited to, the followingfeatures and embodiments. The absolute measurement from current outageand the difference of the current measurement from baseline (or firstin-service inspection) data can be generated to create a steam generatortube bundle to assess tube-to-tube contact wear. Sudden shifts in thespatial relationship between two or more adjacent steam generator tubesbased on comparison of baseline and current measurements can indicatetube proximity issues.

U-bend signal information from two or more outages can be normalized foreach steam generator tube and overlaid to measure differences from thebaseline measurement. In certain embodiments, the recall of data fromone or more previous outages can be obtained using appropriate software.One or more process channels can be created for this information. Datadifference channels can be created with low pass filter to remove suddenanomaly change from the normalizing process and to provide trendinganalysis of the tube bundle proximity progression.

An automated history comparison process can be employed. The processincludes collecting a current non-destructive examination signal;identifying a historical inspection signal from a previous inspection,for example and without limitation, using Enhanced Automated HistoryAddress (EAHA) and full data recall (FDR); and measuring and storingcurrent inspection signal characteristics and historical inspectionsignal characteristics in a database.

The signal characteristics of the current and previous measuredinspection data stored in the database are defined as follows:

-   -   Signal amplitude and phase;    -   Multiple signal measurement modes, such as volts peak-to-peak        (Vpp), volts max rate (Vmr) and the like;    -   Multi-channel comparison;    -   Multiple bobbin coil responses, e.g., differential or absolute;    -   Other signal characteristic include but are not limited to        signal shape, signal width and signal area; and    -   Signal superposition from other design conditions, i.e., support        structure signal, or from service induced conditions, such as        sludge pile deposits or denting.

The automated history comparison process further includes processing andaligning the historical signal for comparison to the current signal. Theprocessing can include transformation and scaling of the data to matchthe data density and inspection direction. The transformation alsoincludes but is not limited to amplitude and phase adjustment due todifferences in inspection tester configuration and tester excitationmodes (multi-plexed vs. simultaneous injection). Signal segments thatcan be compared include, among others:

-   -   Noise exceedances, for example, Free-span Hot Leg (FHL) or        Free-span Cold Leg (FCL); and    -   Region of Interest (ROI) comparison, for example, full support,        Support Edge Hot (SHE) and Support Center Hot (SCH).    -   The comparison of historical and current signals can be made        from signal subtraction, ROI mixing (SP edge mix or SP center        mix).

The automated history comparison process also includes storing thecomparison results in a database. In the automated history comparisonprocess, accurate historical signal identification is essential tocorrect and effective comparison. The comparison algorithm used balancesbetween preserving a degradation signal and suppressing a common modesignal.

An automated process utilizing a high performance database to record andevaluate multiple signal comparison results over several years isprovided. A technique that establishes normal variance based onobservations of earlier years data is utilized. An automated historytrending process can search for and identify variances that are above anormal variance threshold.

An automated history trending process can include collectingnon-destructive examination results and producing a trending curve forvarious comparison results including but not limited to the following:

-   -   Volt, phase;    -   Signal area, signal width;    -   Correlation with other ROIs in the same steam generator tube or        in tubes with similar properties, e.g., Sigma tube or adjacent        tube; and    -   Noise exceedance.

The automated history trending process can further include storing thetrending curve and slope in a database, and establishing the slope andstandard deviation of normal variance for early service years, e.g.,first, 2^(nd) and 3^(rd) in-service inspection comparisons. Furthermore,the automated history trending process includes querying the database todetect sudden slope change with current and prior data comparison; andmapping the query results to a tube sheet map to highlight regions ofconcern and correlate problem areas in the steam generator tube bundle.

While specific embodiments of the invention have been described indetail, it will be appreciated by those skilled in the art that variousmodifications and alternatives to those details could be developed inlight of the overall teachings of the disclosure. Accordingly, theparticular embodiments disclosed are meant to be illustrative only andnot limiting as to the scope of the invention which is to be given thefull breadth of the appended claims and any and all equivalents thereof.

1.-8. (canceled)
 9. A method of employing at least one eddy currentsensor and at least one digital computing device to non-destructivelyassess a current condition of a number of tubes of a steam generator ofa nuclear power plant, the method comprising: collecting at a first timewith a digital computing device and using an eddy current sensorreceived in and advanced through each of at least some of the number oftubes a historic data set for each of at least some of the number oftubes; collecting at a second time with a digital computing device andusing an eddy current sensor received in each of at least some of thenumber of tubes and advanced there through a current data set for eachof at least some of the number of tubes; measuring noise window of thehistorical data set to determine a historical noise baseline; storingthe historical noise baseline in the digital computing device; measuringnoise window of the current data set to determine a current noisebaseline; storing the current noise baseline in the digital computingdevice; comparing the historical noise baseline and the current noisebaseline to determine a difference; and identifying a region of abaseline shift based on the difference to show potential tube to tubecontact or long taper wear.
 10. A method of employing at least one eddycurrent sensor and at least one digital computing device tonon-destructively assess a current condition of a number of tubes of asteam generator of a nuclear power plant, the method comprising:collecting at a first time with a digital computing device and using aneddy current sensor received in and advanced through each of at leastsome of the number of tubes a historic data set for each of at leastsome of the number of tubes; collecting at a second time with a digitalcomputing device and using an eddy current sensor received in each of atleast some of the number of tubes and advanced there through a currentdata set for each of at least some of the number of tubes; measuring asignal of interest using the historical data set and recording at leastone signal characteristic selected from the group consisting of signalamplitude, phase, pattern, signal width, and signal area; storing the atleast one signal characteristic in database; measuring a signal ofinterest using the current data set and recording at least one signalcharacteristic selected from the group consisting of signal amplitude,phase, pattern, signal width, and signal area; storing the at least onesignal characteristic in database; generating a trending plot comparisonbetween the at least one signal characteristic for the historical andcurrent data sets to determine variances there between; determining anormal variances based on the historical data set to determine a normalvariance zone; and determining an abnormal variance based on the currentdata set if the comparison is different than the normal variance. 11.The method of claim 9, wherein the at least one eddy current sensor andthe at least one digital computing device assess bobbin coil eddycurrent data for the historic data set and the current data set.
 12. Themethod of claim 9, wherein the collecting each of the historic data setand the current data set includes performing a total signal and noiseanalysis employing a real time automated analysis computer code.
 13. Themethod of claim 9, wherein the measuring of the noise window of thehistorical data set and the current data set includes identifying tubeU-bends, tube supports and transitions.
 14. The method of claim 9,wherein the measuring of the noise window of the historical data set andthe current data set includes identifying straight length tube sections.15. The method of claim 10, further comprising transforming the historicand current data sets for amplitude and phase adjustment for differencesin inspection tester configuration and tester excitation modes.
 16. Themethod of claim 10, further comprising storing the trending curve andslope in a database, and establishing the slope and standard deviationof normal variance for each in-service inspection.
 17. The method ofclaim 10, further comprising querying the database to detect suddenslope change with current and prior data comparison and mapping thequery results to a tube sheet map to highlight regions of concern.
 18. Amethod of analyzing steam generator bobbin coil data for detection oftube-to-tube contact wear and tube-to-tube proximity, comprising: a.performing a baseline total signal and noise analysis; b. performing abaseline signal analysis of interfering structures, wherein theinterfering structures comprise tube U-bends, tube supports andtransitions; c. performing a baseline signal analysis of straight linetube sections; d. removing the baseline signal analysis of interferingstructures from the baseline total signal and noise analysis forcreating a true measurement of a baseline signal analysis; e. storingthe true measurement of the baseline signal analysis in a database ashistorical data; f. repeating steps a. through e. for a currentinspection to produce current data; g. comparing the historical data andthe current data; and h. identifying changes between the historical dataand the current data, wherein, changes in the straight length tubesections signals indicate a potential tube proximity issue, and whereinchanges in the tube u-bends, tube supports and transitions signalsindicate a potential wear issue.
 9. A method of employing at least oneeddy current sensor and at least one digital computing device tonon-destructively assess a current condition of a number of tubes of asteam generator of a nuclear power plant, the method comprising:collecting at a first time with a digital computing device and using aneddy current sensor received in and advanced through each of at leastsome of the number of tubes a historic data set for each of at leastsome of the number of tubes; collecting at a second time with a digitalcomputing device and using an eddy current sensor received in each of atleast some of the number of tubes and advanced there through a currentdata set for each of at least some of the number of tubes; measuringnoise window of the historical data set to determine a historical noisebaseline; storing the historical noise baseline in the digital computingdevice; measuring noise window of the current data set to determine acurrent noise baseline; storing the current noise baseline in thedigital computing device; comparing the historical noise baseline andthe current noise baseline to determine a difference; and identifying aregion of a baseline shift based on the difference to show potentialtube to tube contact or long taper wear.
 10. A method of employing atleast one eddy current sensor and at least one digital computing deviceto non-destructively assess a current condition of a number of tubes ofa steam generator of a nuclear power plant, the method comprising:collecting at a first time with a digital computing device and using aneddy current sensor received in and advanced through each of at leastsome of the number of tubes a historic data set for each of at leastsome of the number of tubes; collecting at a second time with a digitalcomputing device and using an eddy current sensor received in each of atleast some of the number of tubes and advanced there through a currentdata set for each of at least some of the number of tubes;